The Shapley additive explanations (SHAP) model describes popular NN methods based on GAM and Shapley theory [1]}.
In particular, SHAP shows that DNNs can be described as model-agnostic using DeepLift, which compares the activation of each neuron to its reference activation and assigns contribution scores according to the difference [2]}. The time-SHAP model further explains LSTMs and reduces the computational cost of kernel-SHAP during explanatory training [3]}.
